چكيده به لاتين
Abstract:
Multiple sclerosis is one of the most prevalent neurological disorders in the world. Despite all the efforts, there’s no definite criterion for MS diagnosis. Given that the brain functional connectivity is damaged during MS disease, investigating brain connectome may have useful diagnostic information.
One of the most recent fields of MS researches is using EEG as a diagnostic tool. Already lots of studies have shown the change of brain signal component during MS. Brain connectivity and its related features are of interested areas in pathological and diagnostic MS studies. Using brain signals analysis in conjunction with neuropsychological tests can significantly increase the diagnosis accuracy. Sometimes in MS studies, EEG is recorded when subject is involved in a task, which needs subject’s vulnerable attention. Patient’s brain activity can differ from normal group. Thus, brain connectivity analysis during an attentional task may also have diagnostic information.
In order to study brain connectivity, EEG signals were recorded from MS and normal groups during task an d resting state, using Emotiv system. We studied 6 patients with MS and 8 healthy controls. We constructed for each subject brain networks using coherence and PLI measures and extracted graph features.
Results indicate brain network topological changes during MS. Clustering coefficient decrease and characteristic path length increase in patient group shows decreased optimal connectivity especially in the resting state. Brain networks behaved different during tasks and showed a little improvement in some features. According to the results, we can say that patient’s brain could slightly compensate for lost connections and preserve optimal information transfer.
Keywords: Multiple sclerosis, EEG, brain connectivity, graph theory, coherence, phase lag index
